Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2011 Jul 19;7:510.
doi: 10.1038/msb.2011.37.

Absolute Quantification of Microbial Proteomes at Different States by Directed Mass Spectrometry

Affiliations
Free PMC article

Absolute Quantification of Microbial Proteomes at Different States by Directed Mass Spectrometry

Alexander Schmidt et al. Mol Syst Biol. .
Free PMC article

Abstract

Over the past decade, liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has evolved into the main proteome discovery technology. Up to several thousand proteins can now be reliably identified from a sample and the relative abundance of the identified proteins can be determined across samples. However, the remeasurement of substantially similar proteomes, for example those generated by perturbation experiments in systems biology, at high reproducibility and throughput remains challenging. Here, we apply a directed MS strategy to detect and quantify sets of pre-determined peptides in tryptic digests of cells of the human pathogen Leptospira interrogans at 25 different states. We show that in a single LC-MS/MS experiment around 5000 peptides, covering 1680 L. interrogans proteins, can be consistently detected and their absolute expression levels estimated, revealing new insights about the proteome changes involved in pathogenic progression and antibiotic defense of L. interrogans. This is the first study that describes the absolute quantitative behavior of any proteome over multiple states, and represents the most comprehensive proteome abundance pattern comparison for any organism to date.

Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Figure 1
Figure 1
Global protein profiling workflow. In the first phase of the study (discovery phase), the peptide samples representing different cell states were mixed and analyzed by data-dependent acquisition (DDA) followed by directed 1D-LC–MS/MS. To achieve comprehensive proteome coverage, all detectable precursor ions, referred to as features, were extracted, sequenced in sequential directed LC–MS/MS analyses and identified by database searching. All identified peptide sequences were stored in a 1D-PeptideAtlas together with their precursor ion signal intensity, elution times and mass-to-charge ratio. For each protein, mass and time coordinates from the five most suitable peptides (PTPs) for quantification were extracted from the PeptideAtlas and stored in an inclusion list. Additionally, a spectral library was generated from the identified spectra to improve both, the sensitivity and speed of spectral matching in the quantification phase. In this phase (scoring phase), LC–MS/MS analysis was focused on the pre-selected PTPs as well as a set of heavy labeled reference peptides that were added to each sample. This determined the concentrations of the corresponding proteins in the sample, which could be used as anchor points to translate the MS response of each identified protein into its concentration (Malmström et al, 2009). After spectral matching, label-free quantification was employed to extract and align identified features and monitor their corresponding protein abundances redundantly over all samples.
Figure 2
Figure 2
Directed LC–MS/MS analysis of the L. interrogans proteome. A pool of peptide samples generated from different perturbations was LC–MS analyzed to generate a comprehensive protein/peptide atlas of L. interrogans. (A) This was achieved by accumulating the MS data obtained from (i) two non-directed (DDA) LC–MS runs followed by (ii) directed LC–MS analysis of all detected features, (iii) previously detected PTPs (Beck et al, 2009) and (iv) predicted PTPs (Mallick et al, 2007). Proteins detected with five or more PTPs were excluded in the following analysis. The numbers of identified proteins (y axis) and peptides (inset) versus the number of identified tandem mass spectra are displayed. For comparison, the protein discoveries obtained from a non-directed LC–MS analysis of 24 OGE fractions (OGE/LC) are shown in red. A recently developed algorithm was deployed to estimate the increase in protein/peptide discoveries with additional LC–MS experiments (dashed lines) (Claassen et al, 2009). (B) Venn diagram showing the overlap of proteins identified with the LC-only and the OGE/LC–MS approach. (C) LC–MS map of all identified features (green). Precursor ions identified as tryptic peptides of the GroEL protein are shown in red. The sequences as well as the coordinates of the five PTPs selected for GroEL monitoring in the scoring phase are indicated in blue.
Figure 3
Figure 3
Hierarchical clustering of protein concentration changes. Hierarchical clustering of absolute protein abundance changes to the corresponding untreated control samples in copies/cell (log10) for all 24 treatments. The column dendrogram representing the clustering of the differentially perturbed samples is displayed and the clusters (1–6) obtained are indicated. Significantly enriched (P<0.05) biological processes (BP) based on GO are indicated for all eight protein clusters obtained (a–h).
Figure 4
Figure 4
Pathway analysis of K-means clustered proteome changes. (AD) The individual protein profiles (changes in copies/cell, log10) obtained were grouped and numbered by K-means clustering for each treatment, respectively. Each cluster profile is indicated with the first letter of the treatment and its number. (E) All identified protein clusters were searched for significant overrepresentation of proteins belonging to specific pathways according to the KEGG database (Kanehisa et al, 2010) using DAVID (Huang et al, 2007), respectively. Protein groups that are up-regulated after 24 h of treatment are shown in red, while clusters containing mostly proteins with reduced expression after 24 h are indicated in blue.
Figure 5
Figure 5
Protein dynamics within operons. (A) Absolute protein concentration variance over all proteins (1) and within operons comprising different number of genes (2–6). (B) Like A, representing the variance in protein copies/cell (cpc) after 168 h of serum treatment over all proteins (1) and within operons covering various numbers of genes (2–6). The standard deviations are indicated as error bars. (C) Median protein ratio (log10) of the proteins present in the largest predicted operon in the L. interrogans genome on chromosome I ranging from position 3 455 000 to 3 470 700. The ratios are displayed for 3 h (blue), 12 h (red) and 48 h (green) of serum, doxycycline and ciprofloxacin treatment, respectively.

Similar articles

See all similar articles

Cited by 41 articles

See all "Cited by" articles

References

    1. Ahrens CH, Brunner E, Basler K (2010) Quantitative proteomics: a central technology for systems biology. J Proteom 73: 820–827 - PubMed
    1. Akashi H, Gojobori T (2002) Metabolic efficiency and amino acid composition in the proteomes of Escherichia coli and Bacillus subtilis. Proc Natl Acad Sci USA 99: 3695–3700 - PMC - PubMed
    1. Barnett JK, Barnett D, Bolin CA, Summers TA, Wagar EA, Cheville NF, Hartskeerl RA, Haake DA (1999) Expression and distribution of leptospiral outer membrane components during renal infection of hamsters. Infect Immun 67: 853–861 - PMC - PubMed
    1. Beck M, Malmström JA, Lange V, Schmidt A, Deutsch EW, Aebersold R (2009) Visual proteomics of the human pathogen Leptospira interrogans. Nat Meth 6: 817–823 - PMC - PubMed
    1. Becker D, Selbach M, Rollenhagen C, Ballmaier M, Meyer TF, Mann M, Bumann D (2006) Robust Salmonella metabolism limits possibilities for new antimicrobials. Nature 440: 303–307 - PubMed

Publication types

LinkOut - more resources

Feedback